IEEE Trans Image Process - Vehicle detection in aerial surveillance using dynamic Bayesian networks.

Tópicos

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Resumo

We present an automatic vehicle detection system for aerial surveillance in this paper. In this system, we escape from the stereotype and existing frameworks of vehicle detection in aerial surveillance, which are either region based or sliding window based. We design a pixelwise classification method for vehicle detection. The novelty lies in the fact that, in spite of performing pixelwise classification, relations among neighboring pixels in a region are preserved in the feature extraction process. We consider features including vehicle colors and local features. For vehicle color extraction, we utilize a color transform to separate vehicle colors and nonvehicle colors effectively. For edge detection, we apply moment preserving to adjust the thresholds of the Canny edge detector automatically, which increases the adaptability and the accuracy for detection in various aerial images. Afterward, a dynamic Bayesian network (DBN) is constructed for the classification purpose. We convert regional local features into quantitative observations that can be referenced when applying pixelwise classification via DBN. Experiments were conducted on a wide variety of aerial videos. The results demonstrate flexibility and good generalization abilities of the proposed method on a challenging data set with aerial surveillance images taken at different heights and under different camera angles.

Resumo Limpo

present automat vehicl detect system aerial surveil paper system escap stereotyp exist framework vehicl detect aerial surveil either region base slide window base design pixelwis classif method vehicl detect novelti lie fact spite perform pixelwis classif relat among neighbor pixel region preserv featur extract process consid featur includ vehicl color local featur vehicl color extract util color transform separ vehicl color nonvehicl color effect edg detect appli moment preserv adjust threshold canni edg detector automat increas adapt accuraci detect various aerial imag afterward dynam bayesian network dbn construct classif purpos convert region local featur quantit observ can referenc appli pixelwis classif via dbn experi conduct wide varieti aerial video result demonstr flexibl good general abil propos method challeng data set aerial surveil imag taken differ height differ camera angl

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